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Optimal resource allocation in cellular sensing systems.

ABSTRACT: Living cells deploy many resources to sense their environments, including receptors, downstream signaling molecules, time, and fuel. However, it is not known which resources fundamentally limit the precision of sensing, like weak links in a chain, and which can compensate each other, leading to trade-offs between them. We present a theory for the optimal design of the large class of sensing systems in which a receptor drives a push-pull network. The theory identifies three classes of resources that are required for sensing: receptors and their integration time, readout molecules, and energy (fuel turnover). Each resource class sets a fundamental sensing limit, which means that the sensing precision is bounded by the limiting resource class and cannot be enhanced by increasing another class--the different classes cannot compensate each other. This result yields a previously unidentified design principle, namely that of optimal resource allocation in cellular sensing. It states that, in an optimally designed sensing system, each class of resources is equally limiting so that no resource is wasted. We apply our theory to what is arguably the best-characterized sensing system in biology, the chemotaxis network of Escherichia coli. Our analysis reveals that this system obeys the principle of optimal resource allocation, indicating a selective pressure for the efficient design of cellular sensing systems.

SUBMITTER: Govern CC

PROVIDER: S-EPMC4267345 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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Optimal resource allocation in cellular sensing systems.

Govern Christopher C CC Ten Wolde Pieter Rein PR

Proceedings of the National Academy of Sciences of the United States of America 20141124 49

Living cells deploy many resources to sense their environments, including receptors, downstream signaling molecules, time, and fuel. However, it is not known which resources fundamentally limit the precision of sensing, like weak links in a chain, and which can compensate each other, leading to trade-offs between them. We present a theory for the optimal design of the large class of sensing systems in which a receptor drives a push-pull network. The theory identifies three classes of resources t ...[more]

PMID: 25422473

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Project description:BackgroundThe new waves of COVID-19 outbreaks caused by the SARS-CoV-2 Omicron variant are developing rapidly and getting out of control around the world, especially in highly populated regions. The healthcare capacity (especially the testing resources, vaccination coverage, and hospital capacity) is becoming extremely insufficient as the demand will far exceed the supply. To address this time-critical issue, we need to answer a key question: How can we effectively infer the daily transmission risks in different districts using machine learning methods and thus lay out the corresponding resource prioritization strategies, so as to alleviate the impact of the Omicron outbreaks?MethodsWe propose a computational method for future risk mapping and optimal resource allocation based on the quantitative characterization of spatiotemporal transmission patterns of the Omicron variant. We collect the publicly available data from the official website of the Hong Kong Special Administrative Region (HKSAR) Government and the study period in this paper is from December 27, 2021 to July 17, 2022 (including a period for future prediction). First, we construct the spatiotemporal transmission intensity matrices across different districts based on infection case records. With the constructed cross-district transmission matrices, we forecast the future risks of various locations daily by means of the Gaussian process. Finally, we develop a transmission-guided resource prioritization strategy that enables effective control of Omicron outbreaks under limited capacity.ResultsWe conduct a comprehensive investigation of risk mapping and resource allocation in Hong Kong, China. The maps of the district-level transmission risks clearly demonstrate the irregular and spatiotemporal varying patterns of the risks, making it difficult for the public health authority to foresee the outbreaks and plan the responses accordingly. With the guidance of the inferred transmission risks, the developed prioritization strategy enables the optimal testing resource allocation for integrative case management (including case detection, quarantine, and further treatment), i.e., with the 300,000 testing capacity per day; it could reduce the infection peak by 87.1% compared with the population-based allocation strategy (case number reduces from 20,860 to 2689) and by 24.2% compared with the case-based strategy (case number reduces from 3547 to 2689), significantly alleviating the burden of the healthcare system.ConclusionsComputationally characterizing spatiotemporal transmission patterns allows for the effective risk mapping and resource prioritization; such adaptive strategies are of critical importance in achieving timely outbreak control under insufficient capacity. The proposed method can help guide public-health responses not only to the Omicron outbreaks but also to the potential future outbreaks caused by other new variants. Moreover, the investigation conducted in Hong Kong, China provides useful suggestions on how to achieve effective disease control with insufficient capacity in other highly populated countries and regions.

Project description:BackgroundA key challenge for most systems is how to provide effective access to urgent and emergency care across rural and urban populations. Tensions about the placement and scope of hospital emergency services are longstanding in Irish political life and there has been recent reform to centralise hospital services in some regions. The focus of this paper is a system approach to examine the geographic variation in resourcing and utilisation of such care across GP practices, out-of-hours care, ambulance services, Emergency Departments and Local Injury Units in Ireland.MethodsWe used a cross-sectional study design to evaluate variation in resource allocation by aggregating geographic funding to various elements of the urgent and emergency care system and assessing patterns in hospital resource utilisation across the population. Expenditure, staffing, access and activity data were gathered from government sources, individual facilities and service providers, health professional bodies, private firms and central statistics. Data on costs and activity in 2014 are collated and presented at both county and regional levels. Analyses focus on resources spent on urgent and emergency care across geographic areas, the role of population concentration in allocation, the relationship between pre-hospital spending and in-hospital spending, and the utilisation of hospital-based emergency care resources by residents of each county.ResultsAn array of funding mechanisms exists, resulting in a fragmented approach to the resourcing of urgent and emergency care. There are large differences in spending per capita at the county-level, ranging from between €50 and €200 per capita; however, these are less pronounced regionally. Distribution of hospital emergency care resources is highly skewed to the North East of the country, and away from the recently reconfigured South and Mid-West regions.ConclusionsThis analysis advances the traditional approach of evaluating individual services or hospital resourcing. There are notable differences in utilisation of hospital-based emergency care resources at the regional level, indicating that populations within those regions which have been reconfigured have lower utilisation of hospital resources. There is a clear case for more integration in decision-making around funding and consideration of key principles, such as equity, to guide that process.

Dataset Information

Optimal resource allocation in cellular sensing systems.

Publications

Optimal resource allocation in cellular sensing systems.

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